Personalized recommendation method based on GWO-FCM

Abstract

The invention discloses a personalized recommendation method based on GWO-FCM, and the method comprises the following steps: S1, obtaining data information of a user in a certain time period from a movie watching platform, and obtaining the hobbies and interests of the user; S2, according to the behavior information of the user, extracting movie information by applying an optimized collaborative filtering algorithm to form an algorithm recommendation list; S3, processing an algorithm recommendation list, carrying out screening according to watching records and browsing records of the user, predicting and sorting filtered movie scores and sorted, obtaining an actual recommendation list, and forming personalized recommendation; and S4, arranging the recommended contents in a descending orderaccording to the prediction score, and pushing the specific information of the movie to a corresponding position. According to the method, interests and hobbies among users are fully understood for recommendation, and the personalized recommendation model of the fuzzy Cmean clustering algorithm based on wolf pack algorithm optimization is used, so that the data sparsity can be relieved to a certain extent, and recommendation can be performed more accurately.

Description

technical field [0001] The invention belongs to the technical field of personalized recommendation, and in particular relates to a personalized recommendation method based on GWO-FCM. Background technique [0002] Following the rapid development of the Internet of Things, artificial intelligence, and Internet +, massive data information is transmitted to all parts of the world through the network, which brings great convenience to people's lives, but it is accompanied by the problem of information overload. In this era of information explosion, how to extract effective information from massive data and make reasonable choices has increasingly become the focus of attention. According to the interests and preferences of each user, the personalized recommendation system conducts data mining, selects products that users may be interested in, and realizes personalized recommendations. [0003] Collaborative filtering technology is a common technology for implementing recommendat...

AI Technical Summary

Problems solved by technology

However, in actual operation, the traditional collaborative filtering technology still has the problem of low recommendation accuracy, and there is a large deviation when looking for similar users or similar items.

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